A novel reliability model matching information involved in sample data

Based on stepwise uniform distribution, a novel reliability model is proposed to reasonably match information provided by sample data and that information is not enough to determine probability distributions of random variables. For the sub-intervals formed by the sample data, the corresponding non-probabilistic reliability indexes can be calculated by the non-probabilistic reliability model for interval uncertainty, then the non-probabilistic reliability indexes can be transformed to the probabilistic ones on the assumption of the variables uniformly distributed in the sub-intervals while no frequency information involved. Since each sub-interval holds the same probability emerging in the sample data, the proposed model employs the average of the probabilistic reliabilities of the sub-intervals as the reliability measure of the structure. Comparing with the non-probabilistic reliability model only using the maximum and minimum of the sample data, the proposed model avoids the conservative results by sufficiently using the information involved in sample data. In comparison with the probabilistic model, in which the distributions assumption of the random variables and the parameter estimations are required, the proposed model utilizing the stepwise uniform distribution of the variables avoids the great error of the probabilistic one. The examples show that the proposed model is a reasonable approach between the probabilistic one and the non-probabilistic one since it matches the information involved in the sample data sufficiently, thus it can rationally measure the safety of the structure.